Presentation of clustering-classification heuristic method for improvement accuracy in classification of severity of road accidents in Iran

Alikhani, Mahdi; Nedaie, Ali; Ahmadvand, A M

doi:10.1016/j.ssci.2013.06.008

Cited by 39 publications

(19 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Many clustering and classification algorithms have been used for segmentation or group traffic accidents. Examples include, k-means [28,29], latent class clustering (LCC) [30], and SVM [31]. However, these methods perform like a 'black box' approach and it is difficult to explain the stratification outcome.…”

Section: Spatial Stratified Heterogeneity Detection Of Traffic Accidentsmentioning

confidence: 99%

Geographical Detection of Traffic Accidents Spatial Stratified Heterogeneity and Influence Factors

Zhang

2020

IJERPH

View full text Add to dashboard Cite

The purpose of this paper is to investigate the existence of stratification heterogeneity in traffic accidents in Shenzhen, what factors influence the casualties, and the interaction of those factors. Geographical detection methods are used for the analysis of traffic accidents in Shenzhen. Results show that spatial stratification heterogeneity does exist, and the influencing factors of fatalities and injuries are different. The traffic accident causes and types of primary responsible party have a strong impact on fatalities and injuries, followed by zones and time interval. However, road factors, lighting, topography, etc., only have a certain impact on fatalities. Drunk driving, speeding over 50%, and overloading are more likely to cause more casualties than other illegal behaviors. Speeding over 50% and speeding below 50% have significant different influences on fatalities, while the influences on injuries are not obvious, and so do drunk driving (Blood Alcohol Concentration ≥ 0.08) and driving under the influence of alcohol (0.08 > Blood Alcohol Concentration ≥ 0.02). Both pedestrians and cyclists violating the traffic law are vulnerable to fatality. Heavy truck overloading is more likely to cause major traffic accidents than minibuses. More importantly, there are nonlinear enhanced interactions between the influencing factors, the combination of previous non-significant factors and other factors can have a significant impact on the traffic accident casualties. The findings could be helpful for making differentiated prevention and control measures for traffic accidents in Shenzhen and the method selection of subsequent research.

show abstract

Section: Spatial Stratified Heterogeneity Detection Of Traffic Accidentsmentioning

confidence: 99%

Geographical Detection of Traffic Accidents Spatial Stratified Heterogeneity and Influence Factors

Zhang

2020

IJERPH

View full text Add to dashboard Cite

show abstract

“…In previous studies on the SOM clustering technique application, the distance from a particular neuron to its neighbors has often been calculated using Euclidean [34,37,38] and link functions [39,40]. Comparisons of SOM results using the four distance functions [38,41,42] show that there is no "golden rule" to select a best distance function because each of the three functions, i.e., Euclidean, Manhattan, and link distance, is able to work better than others in different experiments. According to [36,43], the Euclidean and link distance functions are two of the most common for SOM analysis.…”

Section: Self-organizing Map Analysismentioning

confidence: 99%

Variability of Kuroshio Surface Axis Northeast of Taiwan Island Derived from Satellite Altimeter Data

et al. 2020

View full text Add to dashboard Cite

The spatial and temporal variability of the Kuroshio surface axis northeast of Taiwan Island is investigated using 24 years of surface geostrophic currents derived from satellite altimeter data from 1993 to 2016. The Kuroshio surface axis is derived by an extraction method with three selected parameters, including the length of the subsidiary line, the intervals between two adjacent points, and the distance between the two adjacent subsidiary lines. The empirical mode decomposition analysis on the 24-year Kuroshio axes reveals that the mean periods of intra-seasonal and inter-annual variability, which are the two dominant components, are about 3.2 months and 1.3 years, respectively. The self-organizing map analysis reveals that the variation of Kuroshio axis northeast of Taiwan Island has four best matching unit (BMU) patterns: straight-path (BMUS), meandering-path (BMUM) and two transition stages (BMUT1 and BMUT2). The straight-path pattern shows strong seasonality: more likely occurring in summer. The meandering-path pattern is less frequent than straight-path pattern. During a typical period from November 26, 2012 to January 27, 2013, which is chosen as an independent example, the analysis on the satellite altimeter and sea surface temperature data shows that the patterns of the Kuroshio axis change successively in order of BMUT1→BMUM→BMUT2→BMUS, i.e., the Kuroshio axis migrates from the meandering-path to the straight-path pattern. During the typical period the warm water intrusion and a mesoscale eddy occur at the second stage corresponding to BMUM and migrate northwestward gradually at the last two stages corresponding to BMUT2 and BMUS. The transient order appears only during this typical period but it is not common for the whole study period. The monthly mean relatively vorticity is calculated and analyzed to evaluate the impact of the eddies on the Kuroshio surface axis variability, the results show that the anticyclonic (cyclonic) eddies can promote the Kuroshio surface axis to present the meandering-path (straight-path) pattern because of the potential vorticity conservation. The impacts of the anticyclonic eddies and the cyclonic eddies on the variability of the Kuroshio surface axis are opposite. The long-term day-to-day detection contributes to improving understanding the variability of Kuroshio surface axis northeast of Taiwan Island.

show abstract

“…Within Bayesian network analysis, the above eight variables were incorporated as target predictors (i.e., bicycle crash severity); additionally, opponent vehicle type, crash type, and road type were founded to be the most telling predictors of bicycle collision severity, when utilising Bayesian network. Directing a research so as to gain a model for forecasting accident types (damages/causalities) at an appropriate accuracy level, Alikhani et al [13] additionally studied the Adaptive-Neuro Fuzzy Inference System (ANFIS) and the Artificial Neural Network (ANN) accuracy in Iran; for classification of road accident severity within Iran, they explored a heuristic concept of combined clustering-classification system.…”

Section: Faisal Aburub Wael Hadimentioning

confidence: 99%

“…The Classification and Regression Trees (CART) strategy is amongst one of the most popular prediction and classification strategies, and the conclusions they draw may be purposed within decision tree framework (Chang & Wang [14]); as a matter of fact, a number of researchers within traffic safety studios have modified it by leading analysis on 12,604 instances of data related to accidents in Taiwain (via the CART strategy). Alikhani et al [13] suggested that the ANNs (Artificial Neural Networks) are one of the non-parametric strategies and data mining tools whereby the researchers have studied the seriousness of injuries and accidents amongst the individuals involved in such collisions. Using regression analysis, CART technique, and neural networks, Sohn, S.Y.…”

Section: Faisal Aburub Wael Hadimentioning

confidence: 99%

Identifying Seriousness of Car Accidents using Data Mining in Jordanian Roads

2020

IJRTE

View full text Add to dashboard Cite

Abstract: Deemed as the second most common cause of death in Jordan and the second leading cause of death amongst youths and infants, traffic collisions form a serious health problem in Jordan, claiming an average of 1.25 million lives per year and gravely injuring 50 million more globally. This study purposes to explore the utilisation of data mining strategies so as to identify the seriousness of car accidents in Jordan, and then studying them further so as to pinpoint a number of features that impact car accidents.

show abstract

Presentation of clustering-classification heuristic method for improvement accuracy in classification of severity of road accidents in Iran

Cited by 39 publications

References 18 publications

Geographical Detection of Traffic Accidents Spatial Stratified Heterogeneity and Influence Factors

Geographical Detection of Traffic Accidents Spatial Stratified Heterogeneity and Influence Factors

Variability of Kuroshio Surface Axis Northeast of Taiwan Island Derived from Satellite Altimeter Data

Identifying Seriousness of Car Accidents using Data Mining in Jordanian Roads

Contact Info

Product

Resources

About